4.1.1.  OGC SensorThings API

The OGC SensorThings API provides an open and harmonized way to interconnect devices, data, and applications over the web and on the Internet of Things (IoT). At a high level the OGC SensorThings API provides two main parts, namely Part I — Sensing, and Part II — Tasking. The Sensing part provides a standard way to manage and retrieve observations and metadata from a variety of IoT sensor systems. The Tasking part provides a standard way for tasking IoT devices, such as sensors or actuators. The SensorThings API follows REST principles and uses JSON for encoding messages, as well as MQTT for publish/subscribe operations.

4.1.2.  OGC API — Features

The OGC API — Features standard offers the capability to create, modify, and query spatial data on the Web and specifies requirements and recommendations for APIs that want to follow a standard way of sharing feature data. The specification is a multi-part standard. Part 1, labelled the Core, describes the mandatory capabilities that every implementing service has to support and is restricted to read-access to spatial data that is referenced to the World Geodetic System 1984 (WGS 84) Coordinate Reference System (CRS). Part 2 enables the use of different CRS, in addition to the WGS 84. Additional capabilities that address specific needs will be specified in additional parts. Envisaged future capabilities include, for example, support for creating and modifying data, more complex data models, and richer queries.

The OGC API — Features standard is part of the OGC API family of standards. OGC API standards define modular API building blocks to spatially enable Web APIs in a consistent way. The standards make use of the OpenAPI specification for defining the API building blocks.

4.1.3.  OGC Styles and Symbology

The OGC Symbology Conceptual Model: Core Part Standard (OGC 18-067r3), also known as OGC SymCore, specifies the conceptual basis to define symbology rules for the portrayal of geographical data. It is modular and extensible (one core model, many extensions), also encoding agnostic (one symbology model, many encodings). It contains a minimal set of abstract classes representing explicit extension points of the model.

OGC Styles and Symbology is a vision for a standard that implements the OGC Symbology Conceptual Model: Core Part Standard (OGC 18-067r3). The Web Mapping Code Sprint was a key moment to consolidate the roadmap to achieve this vision. The plan towards such a candidate SymCore 2.0 Standard consists of the definition of a conceptual and logical model, Cascading Style Sheets (CSS) and JavaScript Object Notation (JSON) encodings, as well as a mapping to SLD/SE (eventually with existing well known vendor options). The requirements described will be supported by illustrated and encoded cartographic use cases.

4.1.4.  OGC GeoXACML

GeoXACML defines a geo-specific extension to the XACML Policy Language, as defined by the OASIS standard “eXtensible Access Control Markup Language (XACML), that allows managing access to geographic information and services in an interoperable way across jurisdictions. Although XACML is meant to be used as a multi-purpose language, XACML does not have the capabilities to express geo-specific constraints on access rights. GeoXACML provides support for spatial data types and spatial authorization decision functions. Those data types and functions can be used to define additional spatial constraints for XACML based policies. GeoXACML defines extensions for the policy encoding schema only, and thus does not affect the XACML context schema.

Implementations of OGC API Standards are designed to work over HTTP(S). Securing the deployed APIs is not of concern to the OGC API Standards. However, the requirement to describe the API’s endpoint via OpenAPI introduces standard options to enable authentication. Knowing the user that accesses an API is an important aspect for many use cases. Also, user identification is often used to control access to the data, served by the API. But, no OGC API Standard provides guidance how to apply access control. This leads to an interoperability issue if access is denied, but there is no common (standardized) way to “tell the reason”. OGC’s GeoXACML 3.0 (currently still draft) Standard supports the interoperable exchange of access conditions as policies. GeoXACML 3.0 defines the geospatial extension to OASIS XACML 3.0 and thereby extends the ability to include Attribute Based Access Control also on geometries.

The challenge for the GeoXACML part of the code sprint was to develop a Policy Enforcement Point (PEP) that intercepts Web Feature Service (WFS) 2.0 requests and modifies the request according to an access decision returned by a GeoXACML 3.0 policy. The access control use case was the following: “WFS GetFeature requests shall exclude the NYC Central Park for feature type poly_landmarks”.

4.2.1.  OGC GeoPose

OGC GeoPose is an OGC Implementation Standard for exchanging the location and orientation of real or virtual geometric objects (Poses) within reference frames anchored to the earth’s surface (Geo) or within other astronomical coordinate systems. GeoPose specifies a single encoding in JSON format (IETF RFC 8259). The use cases addressed by GeoPose involve interactions between information systems or between an information system and a storage medium. The role of a GeoPose is to convey the orientation and position of a real or virtual object.

4.2.2.  OGC API — Records

OGC API — Records provides discovery and access to metadata records about resources such as features, coverages, tiles / maps, models, assets, services or widgets. The draft specification enables the discovery of geospatial resources by standardizing the way collections of descriptive information about the resources (metadata) are exposed. The draft specification also enables the discovery and sharing of related resources that may be referenced from geospatial resources or their metadata by standardizing the way all kinds of records are exposed and managed. The draft OGC API – Records specification is part of the OGC API family of standards.

4.2.3.  OGC GeoParquet

The Apache Parquet project provides an open source, columnar file format that supports both data storage and retrieval. The OGC GeoParquet specification defines how geospatial data should be stored in Parquet format, including the representation of geometries and the required additional metadata.

4.3.1.  AsyncAPI

AsyncAPI is a specification, by the Linux Foundation, for describing and documenting message-driven APIs in a machine-readable and protocol-agnostic format. The specification defines a set of files required to describe a message-driven API. The files describing the such an API in accordance with the AsyncAPI Specification may be encoded in JSON or YAML.

4.3.2.  STAC API

The SpatioTemporal Asset Catalog (STAC) is a specification that offers a language for describing geospatial information, so it can be worked with, indexed, and discovered. The STAC API offers an interface that implements OGC API — Features. Although STAC has been developed outside of the OGC, in the long term it is envisaged that the STAC API specification will be developed into an OGC Community Standard that implements OGC API building blocks that are relevant for the STAC use cases.

4.4.1.  Apache Arrow

Apache Arrow is a software framework for developing applications that can process columnar data in data analytics solutions. The software framework offers a column-oriented memory format that can represent flat and hierarchical data for analytic operations on modern CPU and GPU hardware. Apache Arrow supports the Apache Parquet Format, which is the format on which the OGC GeoParquet candidate standard is being built to extend.

4.4.2.  Apache Baremaps

Apache Baremaps is an open source software toolkit and infrastructure components for creating, operating, and publishing maps online. The toolkit provides a data pipeline that enables developers to build maps from a variety of data sources. The toolkit enables cartographers to customize the content and the style of a map, as well as supporting services such as location search and IP to location.

4.4.3.  Apache Sedona

Apache Sedona is a high-performance cluster computing system designed specifically for processing large-scale spatial data. The system leverages a number of open-source libraries to execute spatial operations such as Coordinate Reference System (CRS) transformation and file reading.

4.4.4.  Apache SIS

Apache Spatial Information System (SIS) is a free and open source software library for developing geospatial applications. The library is an implementation of OGC GeoAPI 3.0.1 interfaces and can be used for desktop or server applications. Services provided by Apache SIS include metadata, coordinate transformations, filtering and grid coverages. The library is implemented using the Java programming language.

In this code sprint, Apache SIS was used for testing the Geospatial Integrity of Geoscience Software (GIGS) tests runner.

4.5.1.  OSGeo GeoNetwork

GeoNetwork is a catalog application for managing spatially referenced resources. It provides metadata editing and search functions as well as an interactive web map viewer.

4.5.2.  OSGeo GeoServer

GeoServer is a Java-based software server that allows users to view and edit geospatial data. Using open standards by the OGC, GeoServer allows for great flexibility in map creation and data sharing.

4.5.3.  OSGeo OpenLayers

OpenLayers is a library for developing browser-based mapping applications. It works with vector and raster data from a variety of sources and is able to reproject data for rendering. The library has broad support for OGC protocols and formats, including WMS, WMTS, Web Feature Service (WFS), Well Known Text (WKT), Well Known Binary (WKB), Geography Markup Language (GML), GeoTIFF/COG, and KML. In addition, it has support for community and non-OGC standards such as GeoJSON, XYZ tiles, TileJSON, and more. OpenLayers is written in JavaScript and is available under the BSD 2-Clause license.

The ol/source/OGCMapTile module provides a source for map tiles from an OGC API – Tiles implementation. The ol/source/OGCVectorTile module provides a source for vector tiles from an OGC API — Tiles implementation.

4.5.4.  OSGeo OWSLib

OWSLib is a Python client for OGC Web Services and their related content models. The project is an OSGeo Community project and is released under a BSD 3-Clause License.

OWSLib supports numerous OGC standards, including increasing support for the OGC API suite of standards. The official documentation provides an overview of all supported standards.

4.5.5.  OSGeo pycsw

pycsw is an OGC API — Records and OGC CSW server implementation written in Python. Started in 2010 (more formally announced in 2011), pycsw allows for the publishing and discovery of geospatial metadata via numerous APIs (CSW 2/CSW 3, OpenSearch, OAI-PMH, SRU), providing a standards-based metadata and catalogue component of spatial data infrastructures. pycsw is Open Source, released under an MIT license, and runs on all major platforms (Windows, Linux, Mac OS X). pycsw is an official OSGeo Project as well as an OGC Reference Implementation.

pycsw supports numerous metadata content and API standards, including OGC API — Records — Part 1.0: Core and its associated specifications. The official documentation provides an overview of all supported standards.

4.5.6.  OSGeo pygeoapi

pygeoapi is a Python server implementation of the OGC API suite of Standards. The project emerged as part of the next generation OGC API efforts in 2018 and provides the capability for organizations to deploy a RESTful OGC API endpoint using OpenAPI, GeoJSON, and HTML. pygeoapi is open source and released under an MIT license. pygeoapi is an official OSGeo Project as well as an OGC Reference Implementation.

pygeoapi supports numerous OGC API Standards. The official documentation provides an overview of all supported standards.

4.5.7.  OSGeo QGIS

QGIS is a free and open-source cross-platform desktop GIS that supports viewing, editing, and analysis of geospatial data.

4.6.1.  OSGeo ZOO-Project

ZOO-Project is a Web Processing Service (WPS) implementation written in C. It is an open source platform which implements the WPS 1.0.0 and WPS 2.0.0 OGC Standards.

4.6.2.  TEAM Engine

The Test, Evaluation, And Measurement (TEAM) Engine is a testing facility that executes test suites developed using the TestNG framework or the OGC Compliance Test Language (CTL). It is typically used to verify specification compliance and is the official test harness of the OGC Compliance Testing Program (CITE).

4.6.3.  CesiumJS

CesiumJS is an open source JavaScript library that supports visualization of 3D globes and maps on web browsers. The library supports user interaction and streaming in of 3D Tiles (an OGC Community Standard) and a variety of other formats.

4.6.4.  OSGeo Leaflet

Leaflet is an open-source JavaScript library for mobile-friendly interactive maps. It works across all major desktop and mobile platforms, can be extended with a variety of plugins, and offers a well-documented API.

4.6.5.  Camptocamp ogc-client

Camptocamp’s ogc-client product is a Typescript library that implements several OGC Standards. The library supports the Web Map Service (WMS), Web Feature Service (WFS), and OGC API — Features standards. The library also supports the OGC API — Records candidate standard.

4.6.6.  HomeAssistant-SensorThings

Home Assistant is an award-winning free/libre/open-source home automation hub software written in Python. It can interface with over a thousand kinds of IoT devices and services, with a focus on domestic environments and data privacy.

HomeAssistant-SensorThings is a client for the OGC SensorThings standard (part 1) in the form of a Home Assistant plugin (or “integration” in Home Assistant jargon). It implements discovery of available sensors in an endpoint, and periodic updates of the values of those sensors.

4.6.7.  Maplibre

MapLibre GL JS is an open-source library for publishing maps on websites or webview-based applications. The library uses GPU-accelerated rendering to offer client-side display of maps. The library supports client-side rendering of data, which makes it possible for an end-user to customise how a map is displayed.

4.6.8.  OSGeo MDME

MDME (Model Driven Metadata Editor) is a web based application for populating YAML or JSON oriented metadata records. The application is built using Node/Vue.js. The application supports a variety of metadata models, for example MCF, OGC API — Records, and Data Package. The applications support extension of the internal metadata model.

4.6.9.  OL-Cesium

OL-Cesium is a library that integrates OpenLayers and Cesium. The OL-Cesium library supports map contexts, raster data sources, vector data sources, map selection (selected items), and animated transitions between map and globe views.

4.6.10.  OSGeo pygeometa

pygeometa provides a lightweight and Pythonic approach for users to easily create geospatial metadata in standards-based formats using simple configuration files (affectionately called metadata control files (MCF)). The software has minimal dependencies (install is less than 50 kB), and provides a flexible extension mechanism leveraging the Jinja2 templating system. Leveraging the simple but powerful YAML format, pygeometa can generate metadata in numerous standards. Users can also create their own custom metadata formats which can be plugged into pygeometa for custom metadata format output. pygeometa is open source and released under an MIT license.

For developers, pygeometa provides a Pythonic API that allows developers to tightly couple metadata generation within their systems and integrate nicely into metadata production pipelines.

The project supports various metadata formats out of the box including ISO 19115, the WMO Core Metadata Profile, and the WIGOS Metadata Standard. The project also supports the OGC API — Records core record model as well as STAC (Item).

4.6.11.  Geomapfish

GeoMapFish is web application that enables developers to build extensible web-based GIS application. It supports functionality such as webmapping, editing, responsive templates, and customisation. The software supports a variety of OGC Standards, as well as the MapFish protocol for communication between client-side and server-side applications. The software is built using OpenLayers, AngularJS, ngeo, Papyrus, and other toolkits.

4.6.12.  Smapshot

Smapshot is a crowdsourcing platform allowing volunteers to geolocalise in 3D historical images. More than 1’000 volunteers have geolocalised up to 250’000 images. An Open API is available to integrate the data: https://smapshot.heig-vd.ch/development_documentation and the API and georeferencer are open source.

4.6.13.  go-ogc

go-ogc is a Go library for working with OGC API services. The library includes support for OGC API — Common, OGC API — Tiles, and OGC API — Features including support for the CQL2 JSON encoding. The library is available under the Apache 2.0 license.

4.6.14.  GPQ

GPQ is a utility for working with GeoParquet files. The command line interface can be used to validate a GeoParquet file, describe its metadata, and can convert data to and from GeoJSON. The utility is also available as a WebAssembly binary for use in a browser. GPQ is written in Go and is available under the Apache 2.0 license.

5.1.1.  OGC SensorThings API

TBA

5.1.2.  OGC API — Features

TBA

5.1.3.  OGC Styles and Symbology

TBA

5.1.4.  OGC GeoXACML

5.1.4.3.  Achievement

During the Code Sprint, a demo was implemented that follows the XACML flow diagram.

Figure 2 — XACML Flow Diagram

A Geoserver 2.23 WAR-file deployment was used create the WFS 2.0 service endpoint.

The injection of the PEP was done by updating the “geoserver.war” file manually

• zip -u geserver.war WEB-INF/web.xml

• zip -u geoserver.war WEB-INF/lib/GeoXACMLFilter.jar

The web.xml file contains the deployment for the WFS Filter for GeoXACML 3.0.

<filter>
<filter-name>GeoXACMLFilter</filter-name>
<filter-class>de.securedimensions.geoxacml3.ows.GeoServerFilter</filter-class>
<init-param>
<param-name>pdpURL</param-name>
<param-value>https://ogc.demo.secure-dimensions.de/authzforce-ce</param-value>
</init-param>
<init-param>
<param-name>pdpDomain</param-name>
<param-value>A0bdIbmGEeWhFwcKrC9gSQ</param-value>
</init-param>
</filter>
<filter-mapping>
<filter-name>Set Character Encoding</filter-name>
<url-pattern>/*</url-pattern>
</filter-mapping>
<!-- Uncomment following filter to enable CORS
<filter-mapping>
<filter-name>cross-origin</filter-name>
<url-pattern>/*</url-pattern>
</filter-mapping>
-->
<filter-mapping>
<filter-name>GeoXACMLFilter</filter-name>
<url-pattern>/wfs</url-pattern>
</filter-mapping>

Geoserver web.xml initializing the WFS Filter for GeoXACML 3.0 on path /wfs

During the Codesprint one bug concerning the processing of XML attributes was fixed.

5.1.4.4.  Demonstration

During the Code Sprint, the focus was to demonstrate the ability to rewrite WFS requests to reflect geospatial access control decisions. In order to achieve that, a simple GeoXACML 3.0 policy was crafted using ALFA for Visual Studio Code (ALFA plugin).

namespace ogc_codesprint {
    import GeoXACML.*
    import Attributes.*

    obligation Filter = "urn:secd:wfs:filter"

    policyset Manhattan = "root" {
        target clause service == "WFS"
        apply permitOverrides
        policy POLY_LANDMARKS = "poly_landmarks" {
            target clause feature_type == "tiger:poly_landmarks"
            apply firstApplicable
            rule CENTRAL_PARK {
                target clause request == "GetFeature"
                permit
                condition(geometryOneAndOnly(bbox) >< "POLYGON((40.767774 -73.981464, 40.768268 -73.981396, 40.768483 -73.981634, 40.769272 -73.981131, 40.76983 -73.980652, 40.770488 -73.980215, 40.771096 -73.9798, 40.771753 -73.979298, 40.77241 -73.978862, 40.773018 -73.978447, 40.773708 -73.977923, 40.7743 -73.977465, 40.774859 -73.977072, 40.775565 -73.97657, 40.776288 -73.97609, 40.776947 -73.975588, 40.777554 -73.97513, 40.778244 -73.974671, 40.778852 -73.974234, 40.779427 -73.973776, 40.782105 -73.971899, 40.782795 -73.971375, 40.78342 -73.970917, 40.783995 -73.970437, 40.784685 -73.969956, 40.785293 -73.969498, 40.785983 -73.969018, 40.786624 -73.968537, 40.787264 -73.968036, 40.787922 -73.96762, 40.78848 -73.967119, 40.789105 -73.966704, 40.789713 -73.966245, 40.790353 -73.965787, 40.79106 -73.965241, 40.791684 -73.964804, 40.792358 -73.964281, 40.794247 -73.962905, 40.794905 -73.962403, 40.795545 -73.961966, 40.796153 -73.961529, 40.796761 -73.961049, 40.797418 -73.960612, 40.798125 -73.960109, 40.798782 -73.959607, 40.799374 -73.959149, 40.800047 -73.95869, 40.800425 -73.958428, 40.800507 -73.958124, 40.800588 -73.957885, 40.799509 -73.955312, 40.798298 -73.95248, 40.797003 -73.94954, 40.79669 -73.94952, 40.796329 -73.949761, 40.795705 -73.950241, 40.795031 -73.950744, 40.794374 -73.951159, 40.793684 -73.95177, 40.79306 -73.952142, 40.792419 -73.9526, 40.791729 -73.953103, 40.791154 -73.953496, 40.790414 -73.954042, 40.789199 -73.954937, 40.788624 -73.955352, 40.78795 -73.955854, 40.78726 -73.956335, 40.786669 -73.956815, 40.786028 -73.957339, 40.78542 -73.957732, 40.784796 -73.958212, 40.784188 -73.958627, 40.783514 -73.959086, 40.782873 -73.959588, 40.782233 -73.96009, 40.781625 -73.960548, 40.780852 -73.961029, 40.780294 -73.961466, 40.779587 -73.961946, 40.779012 -73.962383, 40.778388 -73.962863, 40.777747 -73.963343, 40.777106 -73.963845, 40.776334 -73.964391, 40.775726 -73.964871, 40.77502 -73.965438, 40.774494 -73.965939, 40.773771 -73.966398, 40.773196 -73.966856, 40.772523 -73.967315, 40.7718 -73.967817, 40.771225 -73.968253, 40.770585 -73.96869, 40.769992 -73.969148, 40.769368 -73.969607, 40.76871 -73.970065, 40.768135 -73.970501, 40.767511 -73.970981, 40.766837 -73.971397, 40.766213 -73.971898, 40.765605 -73.972313, 40.764981 -73.972793, 40.764389 -73.973251, 40.764621 -73.973791, 40.765651 -73.976428, 40.766812 -73.97926, 40.767575 -73.981008, 40.767774 -73.981464))":geometry)
                on permit {
                    obligation Filter {
                        Attributes.Filter.operation = "Disjoint"
                        Attributes.Filter.geometry = "POLYGON((40.767774 -73.981464, 40.768268 -73.981396, 40.768483 -73.981634, 40.769272 -73.981131, 40.76983 -73.980652, 40.770488 -73.980215, 40.771096 -73.9798, 40.771753 -73.979298, 40.77241 -73.978862, 40.773018 -73.978447, 40.773708 -73.977923, 40.7743 -73.977465, 40.774859 -73.977072, 40.775565 -73.97657, 40.776288 -73.97609, 40.776947 -73.975588, 40.777554 -73.97513, 40.778244 -73.974671, 40.778852 -73.974234, 40.779427 -73.973776, 40.782105 -73.971899, 40.782795 -73.971375, 40.78342 -73.970917, 40.783995 -73.970437, 40.784685 -73.969956, 40.785293 -73.969498, 40.785983 -73.969018, 40.786624 -73.968537, 40.787264 -73.968036, 40.787922 -73.96762, 40.78848 -73.967119, 40.789105 -73.966704, 40.789713 -73.966245, 40.790353 -73.965787, 40.79106 -73.965241, 40.791684 -73.964804, 40.792358 -73.964281, 40.794247 -73.962905, 40.794905 -73.962403, 40.795545 -73.961966, 40.796153 -73.961529, 40.796761 -73.961049, 40.797418 -73.960612, 40.798125 -73.960109, 40.798782 -73.959607, 40.799374 -73.959149, 40.800047 -73.95869, 40.800425 -73.958428, 40.800507 -73.958124, 40.800588 -73.957885, 40.799509 -73.955312, 40.798298 -73.95248, 40.797003 -73.94954, 40.79669 -73.94952, 40.796329 -73.949761, 40.795705 -73.950241, 40.795031 -73.950744, 40.794374 -73.951159, 40.793684 -73.95177, 40.79306 -73.952142, 40.792419 -73.9526, 40.791729 -73.953103, 40.791154 -73.953496, 40.790414 -73.954042, 40.789199 -73.954937, 40.788624 -73.955352, 40.78795 -73.955854, 40.78726 -73.956335, 40.786669 -73.956815, 40.786028 -73.957339, 40.78542 -73.957732, 40.784796 -73.958212, 40.784188 -73.958627, 40.783514 -73.959086, 40.782873 -73.959588, 40.782233 -73.96009, 40.781625 -73.960548, 40.780852 -73.961029, 40.780294 -73.961466, 40.779587 -73.961946, 40.779012 -73.962383, 40.778388 -73.962863, 40.777747 -73.963343, 40.777106 -73.963845, 40.776334 -73.964391, 40.775726 -73.964871, 40.77502 -73.965438, 40.774494 -73.965939, 40.773771 -73.966398, 40.773196 -73.966856, 40.772523 -73.967315, 40.7718 -73.967817, 40.771225 -73.968253, 40.770585 -73.96869, 40.769992 -73.969148, 40.769368 -73.969607, 40.76871 -73.970065, 40.768135 -73.970501, 40.767511 -73.970981, 40.766837 -73.971397, 40.766213 -73.971898, 40.765605 -73.972313, 40.764981 -73.972793, 40.764389 -73.973251, 40.764621 -73.973791, 40.765651 -73.976428, 40.766812 -73.97926, 40.767575 -73.981008, 40.767774 -73.981464))":geometry
                    }
                }
            }
            rule ALL_PERMIT {
                permit
            }
        }

    }
}

GeoXACML 3.0 PolicySet described in ALFA

The GeoXACML 3.0 policy is structured in a simple way:

• PolicySet (Manhattan) matches “service == WFS”

• Policy (POLY_LANDMARKS) matches “typesNames == tiger:poly_landmarks”

• Rule (CENTRAL_PARK) matches “request == GetFeature”

• The Rule Condition contains is geospatial “BBOX Intersects Polygon(…)”

Any request that matches the condition results in the decision “Permit” with the Obligation “urn:secd:filter”. As specified in the XACML 3.0 specification, a PEP must enforce the decision including all obligations. The processing of this filter obligation provides the missing information to construct the WFS Filter (disjoint Central Park).

The result of this processing can be visualized with QGIS (WFS Layer):

Figure 3 — Left: Feature type “poly_landmarks” without PEP → Central Park feature(s) are included; Right: Feature type “poly_landmarks” with PEP → Central Park feature(s) are excluded!

The implementation of the Filter obtains the information from the HTTP request:

SubjectCategory subjectCat = new SubjectCategory();
ResourceCategory resourceCat = new ResourceCategory();
ActionCategory actionCategory = new ActionCategory();
EnvironmentCategory environmentCategory = new EnvironmentCategory();

AttributeValueType serviceType = new AttributeValueType(Arrays.asList(httpRequest.getParameter("SERVICE")), XACMLDatatypeId.STRING.value(), null);
Attribute service = new Attribute(Arrays.asList(serviceType),"urn:ogc:ows:service", "", false);
resourceCat.addAttribute(service);

Sample code for obtaining information from the HTTP request

Using the XACML-SDK for Java from Authzforce, the response from the PDP can be obtained in a few lines of code:

Request xacmlRequest = Utils.createXacmlRequest(Arrays.asList(subjectCat), Arrays.asList(resourceCat), Arrays.asList(actionCategory), Arrays.asList(environmentCategory));

ResponsesFactory xacmlResponse = pdp.getAuthZ(subjectCat, resourceCat, actionCategory, environmentCategory);
for (Response r : xacmlResponse.getResponses()) {
    LOGGER.info("XACML Response: " + r.toString());
    DecisionType decision = r.getDecision();
    LOGGER.info("XACML Decision: " + decision.toString());
    LOGGER.info("decision: " + decision.value());
    for (Obligation obligation : r.getObligations().getObligations()) {
        if (obligation.getObligationId().equalsIgnoreCase("urn:secd:wfs:filter")) {
            for (AttributeAssignment aa : obligation.getAttributeAssignments()) {
                if (aa.getAttributeId().equalsIgnoreCase("urn:secd:filter:geometry")) {
                    filterGeometry = aa.getContent().get(0).toString();
                }
                if (aa.getAttributeId().equalsIgnoreCase("urn:secd:filter:operation")) {
                    filterOperation = aa.getContent().get(0).toString();
                }
            }

        }
    }
}

Sample code for obtaining the response from the PDP

<?xml version='1.0' encoding='UTF-8'?><ns4:Response xmlns:ns6="http://authzforce.github.io/pap-dao-flat-file/xmlns/properties/3.6" xmlns:ns5="http://authzforce.github.io/core/xmlns/pdp/8" xmlns:ns4="urn:oasis:names:tc:xacml:3.0:core:schema:wd-17" xmlns:ns3="http://www.w3.org/2005/Atom" xmlns:ns2="http://authzforce.github.io/rest-api-model/xmlns/authz/5"><ns4:Result><ns4:Decision>Permit</ns4:Decision><ns4:Obligations><ns4:Obligation ObligationId="urn:secd:wfs:filter"><ns4:AttributeAssignment AttributeId="urn:secd:filter:operation" Category="urn:oasis:names:tc:xacml:3.0:attribute-category:resource" DataType="http://www.w3.org/2001/XMLSchema#string">Disjoint</ns4:AttributeAssignment><ns4:AttributeAssignment AttributeId="urn:secd:filter:geometry" Category="urn:oasis:names:tc:xacml:3.0:attribute-category:resource" DataType="urn:ogc:def:geoxacml:3.0:data-type:geometry">POLYGON ((40.767774 -73.981464, 40.768268 -73.981396, 40.768483 -73.981634, 40.769272 -73.981131, 40.76983 -73.980652, 40.770488 -73.980215, 40.771096 -73.9798, 40.771753 -73.979298, 40.77241 -73.978862, 40.773018 -73.978447, 40.773708 -73.977923, 40.7743 -73.977465, 40.774859 -73.977072, 40.775565 -73.97657, 40.776288 -73.97609, 40.776947 -73.975588, 40.777554 -73.97513, 40.778244 -73.974671, 40.778852 -73.974234, 40.779427 -73.973776, 40.782105 -73.971899, 40.782795 -73.971375, 40.78342 -73.970917, 40.783995 -73.970437, 40.784685 -73.969956, 40.785293 -73.969498, 40.785983 -73.969018, 40.786624 -73.968537, 40.787264 -73.968036, 40.787922 -73.96762, 40.78848 -73.967119, 40.789105 -73.966704, 40.789713 -73.966245, 40.790353 -73.965787, 40.79106 -73.965241, 40.791684 -73.964804, 40.792358 -73.964281, 40.794247 -73.962905, 40.794905 -73.962403, 40.795545 -73.961966, 40.796153 -73.961529, 40.796761 -73.961049, 40.797418 -73.960612, 40.798125 -73.960109, 40.798782 -73.959607, 40.799374 -73.959149, 40.800047 -73.95869, 40.800425 -73.958428, 40.800507 -73.958124, 40.800588 -73.957885, 40.799509 -73.955312, 40.798298 -73.95248, 40.797003 -73.94954, 40.79669 -73.94952, 40.796329 -73.949761, 40.795705 -73.950241, 40.795031 -73.950744, 40.794374 -73.951159, 40.793684 -73.95177, 40.79306 -73.952142, 40.792419 -73.9526, 40.791729 -73.953103, 40.791154 -73.953496, 40.790414 -73.954042, 40.789199 -73.954937, 40.788624 -73.955352, 40.78795 -73.955854, 40.78726 -73.956335, 40.786669 -73.956815, 40.786028 -73.957339, 40.78542 -73.957732, 40.784796 -73.958212, 40.784188 -73.958627, 40.783514 -73.959086, 40.782873 -73.959588, 40.782233 -73.96009, 40.781625 -73.960548, 40.780852 -73.961029, 40.780294 -73.961466, 40.779587 -73.961946, 40.779012 -73.962383, 40.778388 -73.962863, 40.777747 -73.963343, 40.777106 -73.963845, 40.776334 -73.964391, 40.775726 -73.964871, 40.77502 -73.965438, 40.774494 -73.965939, 40.773771 -73.966398, 40.773196 -73.966856, 40.772523 -73.967315, 40.7718 -73.967817, 40.771225 -73.968253, 40.770585 -73.96869, 40.769992 -73.969148, 40.769368 -73.969607, 40.76871 -73.970065, 40.768135 -73.970501, 40.767511 -73.970981, 40.766837 -73.971397, 40.766213 -73.971898, 40.765605 -73.972313, 40.764981 -73.972793, 40.764389 -73.973251, 40.764621 -73.973791, 40.765651 -73.976428, 40.766812 -73.97926, 40.767575 -73.981008, 40.767774 -73.981464))</ns4:AttributeAssignment></ns4:Obligation></ns4:Obligations>

typeNames[0] = {http://www.census.gov}poly_landmarks
srsName = urn:ogc:def:crs:EPSG::4326
filter = [[ the_geom within POLYGON ((40.46203574999999 -74.35610985937501, 40.46203574999999 -74.103704953125, 40.674587249999995 -74.103704953125, 40.674587249999995 -74.35610985937501, 40.46203574999999 -74.35610985937501)) ] AND [ the_geom disjoint POLYGON ((40.767774 -73.981464, 40.768268 -73.981396, 40.768483 -73.981634, 40.769272 -73.981131, 40.76983 -73.980652, 40.770488 -73.980215, 40.771096 -73.9798, 40.771753 -73.979298, 40.77241 -73.978862, 40.773018 -73.978447, 40.773708 -73.977923, 40.7743 -73.977465, 40.774859 -73.977072, 40.775565 -73.97657, 40.776288 -73.97609, 40.776947 -73.975588, 40.777554 -73.97513, 40.778244 -73.974671, 40.778852 -73.974234, 40.779427 -73.973776, 40.782105 -73.971899, 40.782795 -73.971375, 40.78342 -73.970917, 40.783995 -73.970437, 40.784685 -73.969956, 40.785293 -73.969498, 40.785983 -73.969018, 40.786624 -73.968537, 40.787264 -73.968036, 40.787922 -73.96762, 40.78848 -73.967119, 40.789105 -73.966704, 40.789713 -73.966245, 40.790353 -73.965787, 40.79106 -73.965241, 40.791684 -73.964804, 40.792358 -73.964281, 40.794247 -73.962905, 40.794905 -73.962403, 40.795545 -73.961966, 40.796153 -73.961529, 40.796761 -73.961049, 40.797418 -73.960612, 40.798125 -73.960109, 40.798782 -73.959607, 40.799374 -73.959149, 40.800047 -73.95869, 40.800425 -73.958428, 40.800507 -73.958124, 40.800588 -73.957885, 40.799509 -73.955312, 40.798298 -73.95248, 40.797003 -73.94954, 40.79669 -73.94952, 40.796329 -73.949761, 40.795705 -73.950241, 40.795031 -73.950744, 40.794374 -73.951159, 40.793684 -73.95177, 40.79306 -73.952142, 40.792419 -73.9526, 40.791729 -73.953103, 40.791154 -73.953496, 40.790414 -73.954042, 40.789199 -73.954937, 40.788624 -73.955352, 40.78795 -73.955854, 40.78726 -73.956335, 40.786669 -73.956815, 40.786028 -73.957339, 40.78542 -73.957732, 40.784796 -73.958212, 40.784188 -73.958627, 40.783514 -73.959086, 40.782873 -73.959588, 40.782233 -73.96009, 40.781625 -73.960548, 40.780852 -73.961029, 40.780294 -73.961466, 40.779587 -73.961946, 40.779012 -73.962383, 40.778388 -73.962863, 40.777747 -73.963343, 40.777106 -73.963845, 40.776334 -73.964391, 40.775726 -73.964871, 40.77502 -73.965438, 40.774494 -73.965939, 40.773771 -73.966398, 40.773196 -73.966856, 40.772523 -73.967315, 40.7718 -73.967817, 40.771225 -73.968253, 40.770585 -73.96869, 40.769992 -73.969148, 40.769368 -73.969607, 40.76871 -73.970065, 40.768135 -73.970501, 40.767511 -73.970981, 40.766837 -73.971397, 40.766213 -73.971898, 40.765605 -73.972313, 40.764981 -73.972793, 40.764389 -73.973251, 40.764621 -73.973791, 40.765651 -73.976428, 40.766812 -73.97926, 40.767575 -73.981008, 40.767774 -73.981464)) ]]

5.2.1.  OGC GeoPose

TBA

5.2.2.  OGC API — Records

The OGC API — Records team held two breakouts which focused on advancing the specification for OAB review.

Discussions included:

• extension for faceted searching

• Record model: contacts and roles

• STAC harmonization

5.2.3.  OGC GeoParquet

TBA

5.3.1.  AsyncAPI

TBA

5.3.2.  STAC API

TBA

5.4.1.  Apache Arrow

TBA

5.4.2.  Apache Baremaps

TBA

5.4.3.  Apache Sedona

Apache Sedona leverages a number of open-source libraries, including GeoTools, to execute some spatial operations. However, as an Apache project, Sedona is striving to reduce its reliance on GeoTools due to licensing issues. In order to address this challenge, Sedona is migrating affected functions to Apache SIS.

In this code sprint, sedona maintainers concentrated on replacing GeoTools’ usage in two key functions within Sedona: ST_Transform and the GeoTiff reader. Their focus was on demonstrating how to migrate these functions to Apache SIS, providing a comprehensive tutorial on the necessary steps involved.

5.4.4.  Apache SIS

TBA

5.5.1.  OSGeo GeoNetwork

TBA

5.5.2.  OSGeo GeoServer

The GeoServer team worked remote focusing on seperating our implementation into individually downloadable modules. This work is required in anticpation of cql2 being finalized enabling the first public release of a GeoServer ogcapi module.

• Pull-reqeust (merged) splitting ogcapi implementation into individual downloads

• Pull-request (merged) nightly build docker image allowing public testing of ogcapi modules above

We also checked in with the status of CQL2 which reqmains under active development. We are waiting on this activity before publishing our ogcapi-features module for the general public.

5.5.3.  OSGeo OpenLayers

OpenLayers contributors used time at the sprint to discuss topics related to vector feature styling and rendering. They are developing a new WebGL vector renderer and are working on a new internal feature representation and style encoding syntax to work with the new renderer.

OpenLayers currently allows for very flexible feature style generation by allowing users to provide an arbitrary function to generate symbolizers at render time. This limits the efficiency of vector rendering as we are unable to evaluate these style functions outside the main thread. OpenLayers contributors made good progress at the sprint designing a new encoding for rule based styling that allows flexibility in providing expressions to generate symbolizer values while still allowing for efficient evaluation of these expressions in a worker or on the GPU.

OpenLayers contributors will likely plan another dedicated sprint to continue this work and implement the new styling design in the WebGL vector renderer in addition to the existing Canvas (2D) renderer.

5.6.1.  OSGeo pycsw

The pycsw project upgraded support for STAC API to v1.0.0, and added updates/fixes to its support for OGC API — Features — Part 3: Filtering and Common Query Language (CQL2).

5.6.2.  OSGeo pygeoapi

The pygeoapi project completed the implementation of a feature provider for the ERDDAP data server. ERDDAP is a data server that provides a simple, consistent way to download subsets of scientific datasets in common file formats, graphs and maps. This new pygeoaopi feature was created by WMO as part of the https://docs.wis2box.wis.wmo.int project and has been promoted to be included in the pygeoapi core.

Figure 4 — Screenshot of the pygeoapi OGC API - Features provider for the ERDDAP data server.

An update was also made in pygeoapi to highlight licensing when available as part of a collection definition’s link object (where rel=license).

The pygeoapi project implemented the draft OGC API — Pub/Sub conformance class of OGC API — Environmental Data Retrieval standard (see also original discussion paper (23-013)).

Figure 5 — pygeoapi OGC API - Pub/Sub architecture.

Using OWSLib support for OGC API — Features — Part 4: Create, Replace, Update and Delete, the workflow entailed publsihing new features via transactions, which then triggered push notifiations via the MQTT protocol. Pub/Sub capabilities were made available via the AsyncAPI specification, leveraging the OGC API endpoints as subscription channels.

Figure 6 — pygeoapi OGC API - Pub/Sub architecture.

5.6.3.  OSGeo QGIS

TBA

5.7.1.  OSGeo ZOO-Project

The ZOO-Project worked on moving forward OSGeo Incubation as well as the remaining tasks as part of the Project Graduation Checklist to become an OSGeo project.

5.7.2.  TEAM Engine

The aim of the code sprint was to migrate TEAM Engine to Java 17 and to get it running with the OGC API Features test suite. This is an important part of the upcoming TEAM Engine 6.0 release which will focus on updating Java to version 17, Tomcat to version 10 and Maven dependencies including Java libraries and Maven plugins.

First, a test setup was built using a Dockerfile which contains Java 17, Tomcat 7, the latest build of TEAM Engine 6.0-SNAPSHOT and the OGC API Features tests suite compiled with Java 8.

Then, the code of TEAM Engine was migrated to Java 17 with the aim that the complete built is successful. This work could be completed and it was possible to build and compile TEAM Engine with command mvn clean install site -DskipTests using Java 17. All results were documented in issue 511.

Afterward, TEAM Engine compiled with Java 17 was tested with OGC API Features tests suite using the above described test setup. No errors were detected during testing. The TEAM Engine webapp started, a login via Web Browser Interface was possible and a test run with OGC API Features test suite could be executed. The generated report was displayed as expected and test details could be viewed. Thus, the aim of the code sprint was reached.

In the remaining time, the work concentrated on further tasks being important for the TEAM Engine 6.0 release.

Identified open tasks were documented in separate issues:

• Unit test ImageParserTest.parsePNG_noAlphaChannel fails with Java 17

• Resolve errors and warnings of maven-javadoc-plugin when using Java 17

• Several errors are logged during start up with Java 17

• Analyse warnings logged by maven-pdf-plugin when using Java 17

• Update Maven plugins to latest versions

• Update Maven dependencies to latest versions

Finally, all of them were summarized in milestone 6.0 of TEAM Engine.

5.7.3.  OSGeo Leaflet

TBA

5.7.4.  Camptocamp ogc-client

TBA

5.7.5.  HomeAssistant-SensorThings

The SensorThings standard proved approachable enough so that HomeAssistant-SensorThings could be developed within the span of the codesprint, albeit not being feature complete.

Figure 7 — Partial screenshot of a Home Assistant dashboard showing SensorThings observations from the British Geological Survey, from their FROST server.

The SensorThings standard has some traits that proved very helpful for the development and debugging of the client: the usage of JSON over HTTP, and the availability of full REST-like URIs within the metadata offered by the endpoint.

The nomenclature of concepts posed a minor challenge. An OGC Datastream is a HA sensor, and a OGC Thing is a HA device; and an OGC API endpoint becomes a config entry in HA. The concept of an OGC Observation is implicit in the behaviour of a HA sensor. The OGC concepts of Sensor, Location, FeatureOfInterest and ObservedProperty do not have an equivalent on HA. The HA concepts of area, zone, scene and others do not have an OGC equivalent.

Several concerns have been raised:

• Units of measurement are inconsistent across endpoints. A temperature sensor from USGS uses degC, one from Fraunhofer uses °C, and one from BGS uses C. A human will interpret all those as “degrees celsius”, but this mismatch makes automated data comparisons impossible. The standard recommends using UCUM (Unified Codes for Units of Measure), but none of the SensorThings services explored were following this recommendation.

• The values for the description and definition of ObservedProperty are unreliable, even though they are mandatory fields. All SensorThings services explored were either using empty strings, or reusing the name of an ObservedProperty as its description and/or definition. No servers produce URIs for the ObservedProperty definitions, as mandated by the standard.

• Home Assistant focuses on realtime or quasi-realtime data, and the only viable strategy for SensorThings client is to periodically poll for new observations. However, there is no estimation on how often new observations are made, not even a rough one. Some sensors can provide new observations every second, while others might provide observations once every hour, or once per day. Polling data each second from a daily sensor is a waste of networking resources, whereas polling each day from a secondly sensor introduces an obvious data lag. It’s impossible to choose the right approximate rate of polling. The MQTT extension of the SensorThings specification alleviates this issue, but complicates the implementation of both servers and clients.

• The Home Assistant architecture relies on communicating the last known state of a sensor/device/service; this is arguably the most common use case for internet-connected sensors. However, SensorThings provides no specific facility for this. Instead, the generic way of fetching any and all historical observations must be used, including a query string like ?$top=1&$orderby=phenomenonType desc. The specification states that «the SensorThings API is designed specifically for the resource-constrained IoT devices and the web developer community», but it can be argued that query languages are ill-suited to be implemented in constrained device.

SensorThings is different than other contemporary OGC APIs (such as Maps, Features or Coverages) in that it doesn’t implement content negotiation. It would be interesting to consider content negotiation as an extension, so the last observation could be fetched in human-readable HTML form, or so a series of ovservations could be downloaded as a machine-readable CSV.

Finally, it would be possible to implement a SensorThings server on top of a Home Assistant installation; the scope of such development falls out of a 3-day codesprint, but could be achieved in a timeframe of several weeks. This would provide a cheap way to provide SensorThings endpoints for domestic-grade sensor platforms such as weather stations or soil moisture sensors.

5.7.6.  Maplibre

TBA

5.7.7.  MDME

TBA

5.7.8.  OL-Cesium

During the code sprint, the participants fixed support for the latest Cesium version 1.104. Several of the examples were fixed. Another fix updated the code base so that the Parcel bundler could support better reading of the code base. Support for ECMAScript 6 (ES6) exports was also fixed. The participants also worked on the descruction method to improve memory handling. The participants also worked on the migration to Typescript. Another archievement was the work done on adding polygon extrusion support. Another feature that was implemented during the code sprint was OL-Ceisum support in Geomapfish.

5.7.9.  OSGeo pygeometa

The pygeometa project added support for detailed schema capability reporting, so that a client can be aware of which schemas are supported, and whether those schemas have read and/or write support. This was also implemented as pygeoapi plugin, and made available as part of the pygeoapi public demo. “pygeometa as a service” is used by the live demo of the mdme project. The schema capability integration was added by mdme as part of the sprint.

The MDME project uses vjsf internally to render a web based metadata edit form derived from json schema. A visual edit form for MCF has proven to be usefull in yaml oriented metadata workflows for some user groups. Some json schema concepts such as any-of are not supported by vjsf. During the sprint MDME has been improved to prevent the use of any-of and selector patterns having ‘*’. As mentioned above, import from iso19139 and export to iso19139, stac, and ogcapi:records (based on a transformation service provided by pygeoapi/pygeometa) have been introduced.

5.7.10.  Geomapfish

TBA

5.7.11.  Smapshot

A screenshot of the application, shown during the code sprint, is presented in Figure 8. As illustrated Smapshot enables users to geolocalise historical images in 3D.

Figure 8 — A screenshot of Smapshot

During the code sprint, Geopose format was tested as a way to return position and orientation data of the 3D geolocalized images from smapshot from a standard OGC API features. The Geopose data format has been added to the Open Source NodeJs server of OGC API features: CodeSprint PR. A quick data visualisation has been implemented in Cesium to display the geopose of the camera retrieved from the API with smapshot data and the geopose of the photograph. A screenshot of the application is shown in Figure 9.

Figure 9 — Position and orientation from a geopose represented in Smapshot

5.7.12.  go-ogc

Work was started to add OGC API — Records support to the library. This involved adding an extension mechanism to the existing OGC API — Features structs, in the same way that since the Records spec extends the Features spec. The library is being used in an internal service at Planet Labs, where the OGC API — Features spec is being used to provide metadata about Planet’s imagery catalog. The records branch of the go-ogc library is being used to test an update to the internal service that adds support for OGC API — Records.

{
  "type": "Feature",
  "geometry": {
    "type": "Point",
    "coordinates": [0, 0]
  },
  "properties": {
    "name": "Null Island"
  },
  "time": "2023-05-10T13:00:00Z"
}

{
  "type": "Feature",
  "geometry": {
    "type": "Point",
    "coordinates": [0, 0]
  },
  "properties": {
    "name": "Null Island",
    "time": "2023-05-10T13:00:00Z"
  }
}

5.7.13.  GPQ

The GQP source was recently open sourced, and is now available under the Planet Labs GitHub organization: https://github.com/planetlabs/gpq. A CI workflow was added to publish a WASM binary of the utility to GitHub Pages, allowing converstion of GeoJSON to GeoParquet and vice versa in the browser. In addition, work was done to improve how the Parquet schema is derived from GeoJSON features. Previously, the schema would be derived from the first feature in a collection with all non-null property values. Now, the schema is incrementally built over a range of features, allowing for a more complete schema to be derived. This is especially useful for large collections with sparse properties.